Overview

Dataset statistics

Number of variables21
Number of observations995
Missing cells420
Missing cells (%)2.0%
Duplicate rows6
Duplicate rows (%)0.6%
Total size in memory171.0 KiB
Average record size in memory176.0 B

Variable types

Numeric6
Categorical15

Alerts

Dataset has 6 (0.6%) duplicate rowsDuplicates
Clase_Economica is highly overall correlated with Clase_Ejecutiva and 2 other fieldsHigh correlation
Clase_Ejecutiva is highly overall correlated with Clase_Economica and 2 other fieldsHigh correlation
ComodidadSilla is highly overall correlated with NivelLimpieza and 2 other fieldsHigh correlation
NivelLimpieza is highly overall correlated with ComodidadSilla and 2 other fieldsHigh correlation
SatComidaBebidas is highly overall correlated with ComodidadSilla and 2 other fieldsHigh correlation
SatEntretenimiento is highly overall correlated with ComodidadSilla and 2 other fieldsHigh correlation
Sexo_F is highly overall correlated with Sexo_MHigh correlation
Sexo_M is highly overall correlated with Sexo_FHigh correlation
TipoCliente_Esporadico is highly overall correlated with TipoCliente_FrecuenteHigh correlation
TipoCliente_Frecuente is highly overall correlated with TipoCliente_EsporadicoHigh correlation
TipoViaje_Negocios is highly overall correlated with Clase_Economica and 2 other fieldsHigh correlation
TipoViaje_Personal is highly overall correlated with Clase_Economica and 2 other fieldsHigh correlation
satisfaccion_insatisfecho is highly overall correlated with satisfaccion_satisfechoHigh correlation
satisfaccion_satisfecho is highly overall correlated with satisfaccion_insatisfechoHigh correlation
Clase_MuyEconomicanomica is highly imbalanced (56.3%)Imbalance
Edad has 20 (2.0%) missing valuesMissing
DistanciaREconomicarrida has 20 (2.0%) missing valuesMissing
SatServicioWifi has 20 (2.0%) missing valuesMissing
SatPuntualidad has 20 (2.0%) missing valuesMissing
SatComidaBebidas has 20 (2.0%) missing valuesMissing
ComodidadSilla has 20 (2.0%) missing valuesMissing
SatEntretenimiento has 20 (2.0%) missing valuesMissing
SatServicioAbordo has 20 (2.0%) missing valuesMissing
NivelLimpieza has 20 (2.0%) missing valuesMissing
MinRetrasoSalida has 20 (2.0%) missing valuesMissing
Sexo_F has 20 (2.0%) missing valuesMissing
Sexo_M has 20 (2.0%) missing valuesMissing
TipoCliente_Esporadico has 20 (2.0%) missing valuesMissing
TipoCliente_Frecuente has 20 (2.0%) missing valuesMissing
TipoViaje_Negocios has 20 (2.0%) missing valuesMissing
TipoViaje_Personal has 20 (2.0%) missing valuesMissing
Clase_Economica has 20 (2.0%) missing valuesMissing
Clase_Ejecutiva has 20 (2.0%) missing valuesMissing
Clase_MuyEconomicanomica has 20 (2.0%) missing valuesMissing
satisfaccion_insatisfecho has 20 (2.0%) missing valuesMissing
satisfaccion_satisfecho has 20 (2.0%) missing valuesMissing
SatServicioWifi has 36 (3.6%) zerosZeros
SatPuntualidad has 56 (5.6%) zerosZeros
MinRetrasoSalida has 540 (54.3%) zerosZeros

Reproduction

Analysis started2024-03-08 04:20:02.709155
Analysis finished2024-03-08 04:20:07.511911
Duration4.8 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Edad
Real number (ℝ)

MISSING 

Distinct69
Distinct (%)7.1%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean39.735385
Minimum7
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.5 KiB
2024-03-07T23:20:07.580478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q352
95-th percentile64
Maximum80
Range73
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.361013
Coefficient of variation (CV)0.38658271
Kurtosis-0.77713102
Mean39.735385
Median Absolute Deviation (MAD)12
Skewness-0.070834964
Sum38742
Variance235.96071
MonotonicityNot monotonic
2024-03-07T23:20:07.691082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 28
 
2.8%
45 27
 
2.7%
44 26
 
2.6%
39 25
 
2.5%
47 25
 
2.5%
27 24
 
2.4%
26 24
 
2.4%
33 23
 
2.3%
29 23
 
2.3%
43 22
 
2.2%
Other values (59) 728
73.2%
ValueCountFrequency (%)
7 4
0.4%
8 9
0.9%
9 8
0.8%
10 9
0.9%
11 4
0.4%
12 8
0.8%
13 6
0.6%
14 5
0.5%
15 4
0.4%
16 8
0.8%
ValueCountFrequency (%)
80 2
 
0.2%
74 1
 
0.1%
73 1
 
0.1%
72 1
 
0.1%
71 1
 
0.1%
70 6
0.6%
69 6
0.6%
68 8
0.8%
67 4
0.4%
66 9
0.9%

DistanciaREconomicarrida
Real number (ℝ)

MISSING 

Distinct646
Distinct (%)66.3%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1185.4226
Minimum67
Maximum3995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.5 KiB
2024-03-07T23:20:07.803689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile184
Q1412
median846
Q31739
95-th percentile3323.7
Maximum3995
Range3928
Interquartile range (IQR)1327

Descriptive statistics

Standard deviation1000.9428
Coefficient of variation (CV)0.84437639
Kurtosis0.11228899
Mean1185.4226
Median Absolute Deviation (MAD)518
Skewness1.0815006
Sum1155787
Variance1001886.5
MonotonicityNot monotonic
2024-03-07T23:20:07.918296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2475 7
 
0.7%
337 6
 
0.6%
296 6
 
0.6%
1671 6
 
0.6%
192 6
 
0.6%
967 5
 
0.5%
277 5
 
0.5%
304 5
 
0.5%
109 5
 
0.5%
201 5
 
0.5%
Other values (636) 919
92.4%
(Missing) 20
 
2.0%
ValueCountFrequency (%)
67 1
 
0.1%
77 2
 
0.2%
78 1
 
0.1%
86 2
 
0.2%
89 1
 
0.1%
95 2
 
0.2%
98 1
 
0.1%
101 1
 
0.1%
108 1
 
0.1%
109 5
0.5%
ValueCountFrequency (%)
3995 1
0.1%
3953 1
0.1%
3944 1
0.1%
3904 1
0.1%
3892 1
0.1%
3880 1
0.1%
3873 1
0.1%
3853 1
0.1%
3842 1
0.1%
3836 1
0.1%

SatServicioWifi
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.6%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2.7025641
Minimum0
Maximum5
Zeros36
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size15.5 KiB
2024-03-07T23:20:08.016754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3653619
Coefficient of variation (CV)0.50520981
Kurtosis-0.90512224
Mean2.7025641
Median Absolute Deviation (MAD)1
Skewness0.02176895
Sum2635
Variance1.8642131
MonotonicityNot monotonic
2024-03-07T23:20:08.095329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 234
23.5%
2 225
22.6%
4 189
19.0%
1 182
18.3%
5 109
11.0%
0 36
 
3.6%
(Missing) 20
 
2.0%
ValueCountFrequency (%)
0 36
 
3.6%
1 182
18.3%
2 225
22.6%
3 234
23.5%
4 189
19.0%
5 109
11.0%
ValueCountFrequency (%)
5 109
11.0%
4 189
19.0%
3 234
23.5%
2 225
22.6%
1 182
18.3%
0 36
 
3.6%

SatPuntualidad
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.6%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.0512821
Minimum0
Maximum5
Zeros56
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size15.5 KiB
2024-03-07T23:20:08.168901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5570717
Coefficient of variation (CV)0.5103008
Kurtosis-1.0653741
Mean3.0512821
Median Absolute Deviation (MAD)1
Skewness-0.33232548
Sum2975
Variance2.4244722
MonotonicityNot monotonic
2024-03-07T23:20:08.247475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 224
22.5%
5 222
22.3%
3 172
17.3%
2 152
15.3%
1 149
15.0%
0 56
 
5.6%
(Missing) 20
 
2.0%
ValueCountFrequency (%)
0 56
 
5.6%
1 149
15.0%
2 152
15.3%
3 172
17.3%
4 224
22.5%
5 222
22.3%
ValueCountFrequency (%)
5 222
22.3%
4 224
22.5%
3 172
17.3%
2 152
15.3%
1 149
15.0%
0 56
 
5.6%

SatComidaBebidas
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.6%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.1866667
Minimum0
Maximum5
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.5 KiB
2024-03-07T23:20:08.322543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.331974
Coefficient of variation (CV)0.41798347
Kurtosis-1.1408911
Mean3.1866667
Median Absolute Deviation (MAD)1
Skewness-0.15935663
Sum3107
Variance1.7741547
MonotonicityNot monotonic
2024-03-07T23:20:08.403121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 236
23.7%
3 208
20.9%
5 202
20.3%
2 201
20.2%
1 127
12.8%
0 1
 
0.1%
(Missing) 20
 
2.0%
ValueCountFrequency (%)
0 1
 
0.1%
1 127
12.8%
2 201
20.2%
3 208
20.9%
4 236
23.7%
5 202
20.3%
ValueCountFrequency (%)
5 202
20.3%
4 236
23.7%
3 208
20.9%
2 201
20.2%
1 127
12.8%
0 1
 
0.1%

ComodidadSilla
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.5%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
4.0
294 
5.0
245 
3.0
174 
2.0
147 
1.0
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row5.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 294
29.5%
5.0 245
24.6%
3.0 174
17.5%
2.0 147
14.8%
1.0 115
 
11.6%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:08.487701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:08.564276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 294
30.2%
5.0 245
25.1%
3.0 174
17.8%
2.0 147
15.1%
1.0 115
 
11.8%

Most occurring characters

ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 294
 
10.1%
5 245
 
8.4%
3 174
 
5.9%
2 147
 
5.0%
1 115
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 975
50.0%
4 294
 
15.1%
5 245
 
12.6%
3 174
 
8.9%
2 147
 
7.5%
1 115
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 294
 
10.1%
5 245
 
8.4%
3 174
 
5.9%
2 147
 
5.0%
1 115
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 294
 
10.1%
5 245
 
8.4%
3 174
 
5.9%
2 147
 
5.0%
1 115
 
3.9%

SatEntretenimiento
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.5%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
4.0
259 
5.0
239 
3.0
205 
2.0
157 
1.0
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row1.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 259
26.0%
5.0 239
24.0%
3.0 205
20.6%
2.0 157
15.8%
1.0 115
11.6%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:08.657864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:08.734935image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 259
26.6%
5.0 239
24.5%
3.0 205
21.0%
2.0 157
16.1%
1.0 115
11.8%

Most occurring characters

ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 259
 
8.9%
5 239
 
8.2%
3 205
 
7.0%
2 157
 
5.4%
1 115
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 975
50.0%
4 259
 
13.3%
5 239
 
12.3%
3 205
 
10.5%
2 157
 
8.1%
1 115
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 259
 
8.9%
5 239
 
8.2%
3 205
 
7.0%
2 157
 
5.4%
1 115
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 259
 
8.9%
5 239
 
8.2%
3 205
 
7.0%
2 157
 
5.4%
1 115
 
3.9%

SatServicioAbordo
Categorical

MISSING 

Distinct5
Distinct (%)0.5%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
4.0
275 
3.0
233 
5.0
218 
2.0
138 
1.0
111 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row5.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 275
27.6%
3.0 233
23.4%
5.0 218
21.9%
2.0 138
13.9%
1.0 111
11.2%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:08.827524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:08.909101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 275
28.2%
3.0 233
23.9%
5.0 218
22.4%
2.0 138
14.2%
1.0 111
11.4%

Most occurring characters

ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 275
 
9.4%
3 233
 
8.0%
5 218
 
7.5%
2 138
 
4.7%
1 111
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 975
50.0%
4 275
 
14.1%
3 233
 
11.9%
5 218
 
11.2%
2 138
 
7.1%
1 111
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 275
 
9.4%
3 233
 
8.0%
5 218
 
7.5%
2 138
 
4.7%
1 111
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 275
 
9.4%
3 233
 
8.0%
5 218
 
7.5%
2 138
 
4.7%
1 111
 
3.8%

NivelLimpieza
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.5%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
4.0
249 
3.0
224 
5.0
209 
2.0
160 
1.0
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row5.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 249
25.0%
3.0 224
22.5%
5.0 209
21.0%
2.0 160
16.1%
1.0 133
13.4%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:09.000689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:09.088273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 249
25.5%
3.0 224
23.0%
5.0 209
21.4%
2.0 160
16.4%
1.0 133
13.6%

Most occurring characters

ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 249
 
8.5%
3 224
 
7.7%
5 209
 
7.1%
2 160
 
5.5%
1 133
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 975
50.0%
4 249
 
12.8%
3 224
 
11.5%
5 209
 
10.7%
2 160
 
8.2%
1 133
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 249
 
8.5%
3 224
 
7.7%
5 209
 
7.1%
2 160
 
5.5%
1 133
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 975
33.3%
0 975
33.3%
4 249
 
8.5%
3 224
 
7.7%
5 209
 
7.1%
2 160
 
5.5%
1 133
 
4.5%

MinRetrasoSalida
Real number (ℝ)

MISSING  ZEROS 

Distinct112
Distinct (%)11.5%
Missing20
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean16.112821
Minimum0
Maximum794
Zeros540
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size15.5 KiB
2024-03-07T23:20:09.189870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile76.3
Maximum794
Range794
Interquartile range (IQR)14

Descriptive statistics

Standard deviation44.632996
Coefficient of variation (CV)2.77003
Kurtosis105.30403
Mean16.112821
Median Absolute Deviation (MAD)0
Skewness7.9387729
Sum15710
Variance1992.1043
MonotonicityNot monotonic
2024-03-07T23:20:09.306479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 540
54.3%
1 31
 
3.1%
6 25
 
2.5%
4 20
 
2.0%
3 20
 
2.0%
2 18
 
1.8%
10 16
 
1.6%
5 13
 
1.3%
9 10
 
1.0%
18 10
 
1.0%
Other values (102) 272
27.3%
(Missing) 20
 
2.0%
ValueCountFrequency (%)
0 540
54.3%
1 31
 
3.1%
2 18
 
1.8%
3 20
 
2.0%
4 20
 
2.0%
5 13
 
1.3%
6 25
 
2.5%
7 9
 
0.9%
8 6
 
0.6%
9 10
 
1.0%
ValueCountFrequency (%)
794 1
0.1%
344 1
0.1%
302 1
0.1%
293 1
0.1%
283 1
0.1%
282 1
0.1%
237 1
0.1%
222 1
0.1%
219 1
0.1%
200 1
0.1%

Sexo_F
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
0.0
488 
1.0
487 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 488
49.0%
1.0 487
48.9%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:09.412080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:09.480645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 488
50.1%
1.0 487
49.9%

Most occurring characters

ValueCountFrequency (%)
0 1463
50.0%
. 975
33.3%
1 487
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1463
75.0%
1 487
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1463
50.0%
. 975
33.3%
1 487
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1463
50.0%
. 975
33.3%
1 487
 
16.6%

Sexo_M
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
1.0
488 
0.0
487 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 488
49.0%
0.0 487
48.9%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:09.557222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:09.626285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 488
50.1%
0.0 487
49.9%

Most occurring characters

ValueCountFrequency (%)
0 1462
50.0%
. 975
33.3%
1 488
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1462
75.0%
1 488
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1462
50.0%
. 975
33.3%
1 488
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1462
50.0%
. 975
33.3%
1 488
 
16.7%

TipoCliente_Esporadico
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
0.0
786 
1.0
189 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 786
79.0%
1.0 189
 
19.0%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:09.702857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:09.781017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 786
80.6%
1.0 189
 
19.4%

Most occurring characters

ValueCountFrequency (%)
0 1761
60.2%
. 975
33.3%
1 189
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1761
90.3%
1 189
 
9.7%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1761
60.2%
. 975
33.3%
1 189
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1761
60.2%
. 975
33.3%
1 189
 
6.5%

TipoCliente_Frecuente
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
1.0
786 
0.0
189 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 786
79.0%
0.0 189
 
19.0%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:09.861598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:09.934665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 786
80.6%
0.0 189
 
19.4%

Most occurring characters

ValueCountFrequency (%)
0 1164
39.8%
. 975
33.3%
1 786
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1164
59.7%
1 786
40.3%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1164
39.8%
. 975
33.3%
1 786
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1164
39.8%
. 975
33.3%
1 786
26.9%

TipoViaje_Negocios
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
1.0
683 
0.0
292 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 683
68.6%
0.0 292
29.3%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:10.013241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:10.087471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 683
70.1%
0.0 292
29.9%

Most occurring characters

ValueCountFrequency (%)
0 1267
43.3%
. 975
33.3%
1 683
23.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1267
65.0%
1 683
35.0%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1267
43.3%
. 975
33.3%
1 683
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1267
43.3%
. 975
33.3%
1 683
23.4%

TipoViaje_Personal
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
0.0
683 
1.0
292 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 683
68.6%
1.0 292
29.3%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:10.171841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:10.250418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 683
70.1%
1.0 292
29.9%

Most occurring characters

ValueCountFrequency (%)
0 1658
56.7%
. 975
33.3%
1 292
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1658
85.0%
1 292
 
15.0%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1658
56.7%
. 975
33.3%
1 292
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1658
56.7%
. 975
33.3%
1 292
 
10.0%

Clase_Economica
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
0.0
544 
1.0
431 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 544
54.7%
1.0 431
43.3%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:10.335495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:10.407724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 544
55.8%
1.0 431
44.2%

Most occurring characters

ValueCountFrequency (%)
0 1519
51.9%
. 975
33.3%
1 431
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1519
77.9%
1 431
 
22.1%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1519
51.9%
. 975
33.3%
1 431
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1519
51.9%
. 975
33.3%
1 431
 
14.7%

Clase_Ejecutiva
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
0.0
519 
1.0
456 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 519
52.2%
1.0 456
45.8%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:10.488841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:10.560253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 519
53.2%
1.0 456
46.8%

Most occurring characters

ValueCountFrequency (%)
0 1494
51.1%
. 975
33.3%
1 456
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1494
76.6%
1 456
 
23.4%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1494
51.1%
. 975
33.3%
1 456
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1494
51.1%
. 975
33.3%
1 456
 
15.6%

Clase_MuyEconomicanomica
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
0.0
887 
1.0
 
88

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 887
89.1%
1.0 88
 
8.8%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:10.639969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:10.710308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 887
91.0%
1.0 88
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 1862
63.7%
. 975
33.3%
1 88
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1862
95.5%
1 88
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1862
63.7%
. 975
33.3%
1 88
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1862
63.7%
. 975
33.3%
1 88
 
3.0%

satisfaccion_insatisfecho
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
1.0
549 
0.0
426 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 549
55.2%
0.0 426
42.8%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:10.791883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:10.864993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 549
56.3%
0.0 426
43.7%

Most occurring characters

ValueCountFrequency (%)
0 1401
47.9%
. 975
33.3%
1 549
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1401
71.8%
1 549
 
28.2%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1401
47.9%
. 975
33.3%
1 549
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1401
47.9%
. 975
33.3%
1 549
 
18.8%

satisfaccion_satisfecho
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size66.2 KiB
0.0
549 
1.0
426 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2925
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 549
55.2%
1.0 426
42.8%
(Missing) 20
 
2.0%

Length

2024-03-07T23:20:10.947573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:11.030710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 549
56.3%
1.0 426
43.7%

Most occurring characters

ValueCountFrequency (%)
0 1524
52.1%
. 975
33.3%
1 426
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1950
66.7%
Other Punctuation 975
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1524
78.2%
1 426
 
21.8%
Other Punctuation
ValueCountFrequency (%)
. 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2925
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1524
52.1%
. 975
33.3%
1 426
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2925
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1524
52.1%
. 975
33.3%
1 426
 
14.6%

Interactions

2024-03-07T23:20:05.999225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:03.435483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:03.954652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.477641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.988627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.481176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:06.085808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:03.522562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.043065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.559219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.072208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.562811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:06.180398image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:03.619153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.133900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.646803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.161125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.656399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:06.262533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:03.702577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.218059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.725877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.239086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.733471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:06.354352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:03.783154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.302471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.813459image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.319159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.832064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:06.436930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:03.864508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.384049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:04.894036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.396733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:20:05.913642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-07T23:20:11.108283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Clase_EconomicaClase_EjecutivaClase_MuyEconomicanomicaComodidadSillaDistanciaREconomicarridaEdadMinRetrasoSalidaNivelLimpiezaSatComidaBebidasSatEntretenimientoSatPuntualidadSatServicioAbordoSatServicioWifiSexo_FSexo_MTipoCliente_EsporadicoTipoCliente_FrecuenteTipoViaje_NegociosTipoViaje_Personalsatisfaccion_insatisfechosatisfaccion_satisfecho
Clase_Economica1.0000.8320.2750.000-0.001-0.0440.0590.000-0.0270.055-0.0650.044-0.0300.0000.0000.0990.0990.5020.5020.4230.423
Clase_Ejecutiva0.8321.0000.2900.0000.0190.026-0.0130.000-0.0110.0630.0680.0420.0250.0000.0000.0650.0650.5290.5290.4930.493
Clase_MuyEconomicanomica0.2750.2901.0000.000-0.0310.030-0.0780.0570.0640.058-0.0060.0000.0080.0000.0000.0390.0390.0360.0360.1180.118
ComodidadSilla0.0000.0000.0001.0000.1730.162-0.0530.5840.6200.5590.0020.0850.1000.0000.0000.0000.0000.1360.1360.0400.040
DistanciaREconomicarrida-0.0010.019-0.0310.1731.0000.049-0.0100.0670.0160.040-0.0570.0210.0020.0000.0000.0000.0000.0060.0060.0000.000
Edad-0.0440.0260.0300.1620.0491.0000.0180.0810.0310.0680.0620.0460.0120.0000.0000.0000.0000.0000.0000.0000.000
MinRetrasoSalida0.059-0.013-0.078-0.053-0.0100.0181.0000.045-0.0410.000-0.0310.000-0.0500.0290.0290.0440.0440.0590.0590.0000.000
NivelLimpieza0.0000.0000.0570.5840.0670.0810.0451.0000.6680.6250.0080.0540.1210.0000.0000.0540.0540.0230.0230.0000.000
SatComidaBebidas-0.027-0.0110.0640.6200.0160.031-0.0410.6681.0000.6120.0100.0000.1350.0000.0000.0000.0000.0590.0590.0000.000
SatEntretenimiento0.0550.0630.0580.5590.0400.0680.0000.6250.6121.000-0.0140.3540.2030.0000.0000.0000.0000.0740.0740.0400.040
SatPuntualidad-0.0650.068-0.0060.002-0.0570.062-0.0310.0080.010-0.0141.0000.0700.3960.0000.0000.0000.0000.0000.0000.0000.000
SatServicioAbordo0.0440.0420.0000.0850.0210.0460.0000.0540.0000.3540.0701.0000.0710.0370.0370.0000.0000.0000.0000.0410.041
SatServicioWifi-0.0300.0250.0080.1000.0020.012-0.0500.1210.1350.2030.3960.0711.0000.0000.0000.0000.0000.0000.0000.0210.021
Sexo_F0.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0370.0001.0000.9980.0000.0000.0000.0000.0000.000
Sexo_M0.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0370.0000.9981.0000.0000.0000.0000.0000.0000.000
TipoCliente_Esporadico0.0990.0650.0390.0000.0000.0000.0440.0540.0000.0000.0000.0000.0000.0000.0001.0000.9970.3160.3160.1540.154
TipoCliente_Frecuente0.0990.0650.0390.0000.0000.0000.0440.0540.0000.0000.0000.0000.0000.0000.0000.9971.0000.3160.3160.1540.154
TipoViaje_Negocios0.5020.5290.0360.1360.0060.0000.0590.0230.0590.0740.0000.0000.0000.0000.0000.3160.3161.0000.9980.4190.419
TipoViaje_Personal0.5020.5290.0360.1360.0060.0000.0590.0230.0590.0740.0000.0000.0000.0000.0000.3160.3160.9981.0000.4190.419
satisfaccion_insatisfecho0.4230.4930.1180.0400.0000.0000.0000.0000.0000.0400.0000.0410.0210.0000.0000.1540.1540.4190.4191.0000.998
satisfaccion_satisfecho0.4230.4930.1180.0400.0000.0000.0000.0000.0000.0400.0000.0410.0210.0000.0000.1540.1540.4190.4190.9981.000

Missing values

2024-03-07T23:20:07.049509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-07T23:20:07.283309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EdadDistanciaREconomicarridaSatServicioWifiSatPuntualidadSatComidaBebidasComodidadSillaSatEntretenimientoSatServicioAbordoNivelLimpiezaMinRetrasoSalidaSexo_FSexo_MTipoCliente_EsporadicoTipoCliente_FrecuenteTipoViaje_NegociosTipoViaje_PersonalClase_EconomicaClase_EjecutivaClase_MuyEconomicanomicasatisfaccion_insatisfechosatisfaccion_satisfecho
17.01012.03.05.05.02.05.02.05.026.01.00.00.01.01.00.00.01.00.00.01.0
239.02204.01.01.03.04.04.04.03.00.01.00.00.01.00.01.01.00.00.01.00.0
39.02475.04.02.04.05.01.05.05.00.00.01.00.01.01.00.00.01.00.00.01.0
456.03344.00.05.03.04.02.02.05.00.00.01.00.01.01.00.00.01.00.01.00.0
537.01954.04.02.03.03.04.04.04.010.00.01.00.01.01.00.00.01.00.00.01.0
621.03179.05.05.05.05.05.02.05.00.01.00.00.01.01.00.00.01.00.00.01.0
751.03107.03.03.04.05.05.05.03.00.01.00.00.01.01.00.01.00.00.00.01.0
847.0427.05.03.03.01.05.05.02.02.00.01.00.01.01.00.00.01.00.00.01.0
952.0484.02.02.04.04.04.04.03.00.00.01.00.01.00.01.01.00.00.01.00.0
1037.02486.03.01.04.04.04.02.04.011.00.01.00.01.01.00.00.01.00.01.00.0
EdadDistanciaREconomicarridaSatServicioWifiSatPuntualidadSatComidaBebidasComodidadSillaSatEntretenimientoSatServicioAbordoNivelLimpiezaMinRetrasoSalidaSexo_FSexo_MTipoCliente_EsporadicoTipoCliente_FrecuenteTipoViaje_NegociosTipoViaje_PersonalClase_EconomicaClase_EjecutivaClase_MuyEconomicanomicasatisfaccion_insatisfechosatisfaccion_satisfecho
413NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.00.01.01.00.00.01.00.00.01.0
416NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.00.01.01.00.00.01.00.01.00.0
445NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.01.00.01.00.00.01.00.0
477NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.00.01.01.00.00.01.00.0
508NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.00.01.01.00.00.01.00.00.01.0
509NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.01.00.00.01.00.00.01.0
541NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.00.01.01.00.00.01.00.0
883NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.01.00.00.01.00.00.01.0
932NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.00.01.01.00.00.01.00.00.01.0
966NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.01.00.01.00.00.01.00.01.00.0

Duplicate rows

Most frequently occurring

EdadDistanciaREconomicarridaSatServicioWifiSatPuntualidadSatComidaBebidasComodidadSillaSatEntretenimientoSatServicioAbordoNivelLimpiezaMinRetrasoSalidaSexo_FSexo_MTipoCliente_EsporadicoTipoCliente_FrecuenteTipoViaje_NegociosTipoViaje_PersonalClase_EconomicaClase_EjecutivaClase_MuyEconomicanomicasatisfaccion_insatisfechosatisfaccion_satisfecho# duplicates
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.00.01.01.00.00.01.00.04
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.01.00.00.01.00.00.01.04
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.00.01.01.00.00.01.00.00.01.04
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.01.00.01.01.00.01.00.00.01.00.02
3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.00.01.00.01.01.00.00.01.00.02
5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00.00.01.01.00.00.01.00.01.00.02